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Article published In: The Mental Lexicon
Vol. 19:3 (2024) ► pp.439–495

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Orthographic uncertainty

An entropy-based measure of word form typicality

ORCiD logo with link | University of Alberta
 | McGill University
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Available under the Creative Commons Attribution (CC BY) 4.0 license.

For any use beyond this license, please contact the publisher at rights@benjamins.nl.

Open Access publication of this article was funded through a Transformative Agreement with University of Alberta.

Published online: 6 February 2025
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https://doi.org/10.1075/ml.24006.wes
Abstract
Measures of orthographic typicality have long been studied as predictors of lexical access. The best-known orthographic typicality measure is orthographic neighbourhood size (Coltheart’s N or ON), the number of words that are one letter different, by substitution, from the target word. A more recent related measure of orthographic typicality is orthographic Levenshtein distance 20 (OLD20), the average Levenshtein orthographic edit distance of a target word from its 20 closest neighbours (Yarkoni, T., Balota, D., & Yap, M. (2008). Moving beyond Coltheart’s N: A new measure of orthographic similarity. Psychonomic Bulletin & Review, 15(5), 971–979. Google Scholar logo with link to Google Scholar). Both measures have been implicated in lexical access. In this paper, we propose and assess a family of measures of word form similarity we call orthographic uncertainty. These measures are based on Shannon entropy (Shannon, C. E. (1948). A note on the concept of entropy. Bell System Technical Journal, 271, 379–423. Google Scholar logo with link to Google Scholar), which has a long history of being considered psychologically relevant. Orthographic uncertainty measures are superior to ON and OLD20 at predicting lexical decision and naming reaction times and accuracies. They are also superior to the older measures insofar as they are naturally tied to the widely-accepted quantification using Shannon Entropy of the psychological functions of familiarity, uncertainty, learnability, and representational and computational efficiency.
Article outline
  • Measures of orthographic typicality
  • Other measures of orthographic typicality
    • Non-positional N-gram measures
    • Positional N-gram measures
  • N-gram entropy measures
    • Non-positional N-gram entropy
    • Positional N-gram entropy
  • A new measure of orthographic uncertainty
  • Analysis of the properties of OU
  • Study 1: Predicting behavioral measures of lexical access
    • LD RT
    • LD accuracy
    • Naming RT
    • Naming accuracy
    • Study 1: Discussion
  • Study 2: Predicting human judgments of orthographic typicality
    • Method
    • Participants
      • Stimuli
    • Results
    • Discussion
  • Study 3: Cross-linguistic analysis
    • Method
    • Results
    • Discussion
  • General discussion
  • Open practices statement
  • Notes
  • References
References (79)
References
Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov & F. Caski (Eds.), Proceedings of the Second International Symposium on Information Theory (pp. 267–281). Budapest: Akademiai Kiado.Google Scholar logo with link to Google Scholar
(1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 191, 716–723. Google Scholar logo with link to Google Scholar
Andrews, S. (1989). Frequency and neighborhood effects on lexical access: Activation or search? Journal of Experimental Psychology: Learning, Memory, and Cognition, 151, 802–814.Google Scholar logo with link to Google Scholar
(1992). Frequency and neighborhood effects on lexical access: Lexical similarity or orthographic redundancy? Journal of Experimental Psychology: Learning, Memory, and Cognition, 181, 234–254.Google Scholar logo with link to Google Scholar
(1997). The effect of orthographic similarity on lexical retrieval: Resolving neighborhood conflicts. Psychonomic Bulletin & Review, 4(4), 439–461. Google Scholar logo with link to Google Scholar
Assink, E. M., Kattenberg, G., & Wortmann, C. (1998). Exploring the boundaries of sublexical word identification units: The use of onsets and rimes and reading ability. Journal of psycholinguistic research, 271, 639–659. Google Scholar logo with link to Google Scholar
Balota, D. A., Yap, M. J., Hutchison, K. A., Cortese, M. J., Kessler, B., Loftis, B., Neely, J., Nelson, D. L., Simpson, G. B., & Treiman, R. (2007). The English lexicon project. Behavior Research Methods, 39(3), 445–459. Google Scholar logo with link to Google Scholar
Bateson, G. (1972). Steps to an ecology of mind. New York, NT: Ballantine.Google Scholar logo with link to Google Scholar
Bentz, C., Alikaniotis, D., Cysouw, M., & Ferrer-i-Cancho, R. (2017). The entropy of words — Learnability and expressivity across more than 1000 languages. Entropy, 19(6), 275. Google Scholar logo with link to Google Scholar
Berlyne, D. E. (1971). Aesthetics and Psychobiology. New York: Appleton-Century-Crofts.Google Scholar logo with link to Google Scholar
Blais, C., Fiset, D., Arguin, M., Jolicoeur, P., Bub, D., & Gosselin, F. (2009). Reading between eye saccades. PLoS One, 4(7), e6448. Google Scholar logo with link to Google Scholar
Blevins, J. P. (2013). The information-theoretic turn. Psihologija, 46(4), 355–375. Google Scholar logo with link to Google Scholar
Booth, J. R., & Perfetti, C. A. (2002). Onset and rime structure influences naming but not early word identification in children and adults. Scientific Studies of Reading, 6(1), 1–23. Google Scholar logo with link to Google Scholar
Bowey, J. A. (1990). Orthographic onsets and rimes as functional units of reading. Memory & Cognition, 18(4), 419–427. Google Scholar logo with link to Google Scholar
Carhart-Harris, R. L., Leech, R., Hellyer, P. J., Shanahan, M., Feilding, A., Tagliazucchi, E., Chialvo, D. R., & Nutt, D. (2014). The entropic brain: a theory of conscious states informed by neuroimaging research with psychedelic drugs. Frontiers in human neuroscience, 81, 20. Google Scholar logo with link to Google Scholar
Carreiras, M., Perea, M., & Grainger, J. (1997). Effects of the orthographic neighborhood in visual word recognition: Cross-task comparisons. Journal of experimental psychology: learning, memory, and cognition, 23(4), 857.Google Scholar logo with link to Google Scholar
Chaitin, G. J. (1975). A theory of program size formally identical to information theory. Journal of the ACM, 22(3), 329–340. Google Scholar logo with link to Google Scholar
Chen, Q., & Mirman, D. (2012). Competition and cooperation among similar representations: toward a unified account of facilitative and inhibitory effects of lexical neighbors. Psychological review, 119(2), 417. Google Scholar logo with link to Google Scholar
Coltheart, M., Davelaar, E., Jonasson, J., & Besner, D. (1977). Access to the internal lexicon. In S. Dornic (Ed.), Attention and performance VI: Proceedings of the Sixth International Symposium on Attention and Performance, Stockholm, Sweden, July 28-August 1, 1975. Hillsdale, N.J: Lawrence Erlbaum.Google Scholar logo with link to Google Scholar
Dahan, D., Magnuson, J. S., & Tanenhaus, M. K. (2001). Time course of frequency effects in spoken-word recognition: Evidence from eye movements. Cognitive psychology, 42(4), 317–367. Google Scholar logo with link to Google Scholar
Davis, C. J., Perea, M., & Acha, J. (2009). Re (de) fining the orthographic neighborhood: The role of addition and deletion neighbors in lexical decision and reading. Journal of Experimental Psychology: Human Perception and Performance, 35(5), 1550.Google Scholar logo with link to Google Scholar
del Prado Martın, F. M., Kostić, A., & Baayen, R. H. (2004). Putting the bits together: An information theoretical perspective on morphological processing. Cognition, 94(1), 1–18. Google Scholar logo with link to Google Scholar
Duñabeitia, J. A., & Vidal-Abarca, E. (2008). Children like dense neighborhoods: Orthographic neighborhood density effects in novel readers. Spanish Journal of Psychology, 11(1), 26. Google Scholar logo with link to Google Scholar
Dye, M., Johns, B. T., Jones, M. N., & Ramscar, M. (2016). The structure of names in memory: Deviations from uniform entropy impair memory for linguistic sequences. In A. Papafragou, D. Grodner, D. Mirman, & J. C. Trueswell (Eds.), Proceedings of the 38th Annual Conference of the Cognitive Science Society (pp. 1763–1768). Austin, TX: Cognitive Science Society.
Dye, M., Milin, P., Futrell, R. & Ramscar, M. (2017). A functional theory of gender paradigms. In: Kiefer, F., Blevins, J. P. and Bartos, H., (eds.) Perspectives on Morphological Structure: Data and Analyses. Brill, Leiden, pp. 212–239. Google Scholar logo with link to Google Scholar
Gibson, E., Futrell, R., Piantadosi, S. P., Dautriche, I., Mahowald, K., Bergen, L., & Levy, R. (2019). How efficiency shapes human language. Trends in cognitive sciences, 23(5), 389–407. Google Scholar logo with link to Google Scholar
Grainger, J., & Jacobs, A. M. (1993). Masked partial-word priming in visual word recognition: Effects of positional letter frequency. Journal of Experimental Psychology-Human Perception and Performance, 19(5), 951–964. Google Scholar logo with link to Google Scholar
(1996). Orthographic processing in visual word recognition: a multiple read-out model. Psychological Review, 103(3), 518. Google Scholar logo with link to Google Scholar
Hale, J. (2016). Information‐theoretical complexity metrics. Language and Linguistics Compass, 10(9), 397–412. Google Scholar logo with link to Google Scholar
Harati, P., Westbury, C., & Kiaee, M. (2021). Evaluating the predication model of metaphor comprehension: Using word2vec to model best/worst quality judgments of 622 novel metaphors. Behavior Research Methods, 53(5), 2214–2225. Google Scholar logo with link to Google Scholar
Hastie, T. J. & Tibshirani, R. J. (1990). Generalized Additive Models. Chapman & Hall/CRC.Google Scholar logo with link to Google Scholar
Heilbron, M., Armeni, K., Schoffelen, J. M., Hagoort, P., & De Lange, F. P. (2022). A hierarchy of linguistic predictions during natural language comprehension. Proceedings of the National Academy of Sciences, 119(32), e2201968119. Google Scholar logo with link to Google Scholar
Hirsh, J. B., Mar, R. A., & Peterson, J. B. (2012). Psychological entropy: a framework for understanding uncertainty-related anxiety. Psychological review, 119(2), 304–320. Google Scholar logo with link to Google Scholar
Hollis, G. (2018). Scoring best-worst data in unbalanced many-item designs, with applications to crowdsourcing semantic judgments. Behavior Research Methods, 50(2), 711–729. Google Scholar logo with link to Google Scholar
(2020). The role of number of items per trial in best–worst scaling experiments. Behavior Research Methods, 52(2), 694–722. Google Scholar logo with link to Google Scholar
Hollis, G., & Westbury, C. (2006). NUANCE: Naturalistic University of Alberta nonlinear correlation explorer. Behavior Research Methods, 38(1), 8–23. Google Scholar logo with link to Google Scholar
(2018). When is best-worst best? A comparison of best-worst scaling, numeric estimation, and rating scales for collection of semantic norms. Behavior Research Methods, 501, 115–133. Google Scholar logo with link to Google Scholar
Hollis, G., Westbury, C. F., & Peterson, J. B. (2006). NUANCE 3.0: Using genetic programming to model variable relationships. Behavior research methods, 38(2), 218–228. Google Scholar logo with link to Google Scholar
Huntsman, L. A., & Lima, S. D. (1996). Orthographic neighborhood structure and lexical access. Journal of Psycholinguistic Research, 25(3), 417–429. Google Scholar logo with link to Google Scholar
Keuleers, E. (2013). vwr r package (v. 3). Downloaded from: [URL]
Keuleers, E., Lacey, P., Rastle, K., & Brysbaert, M. (2012). The British Lexicon Project: lexical decision data for 28,730 monosyllabic and disyllabic English words. Behavior Research Methods, 44(1), 287–304. Google Scholar logo with link to Google Scholar
Kiritchenko, S., & Mohammad, S. M. (2016). Capturing reliable fine-grained sentiment associations by crowdsourcing and best-worst scaling. San Diego: Paper presented at the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL).
Levenshtein, V. I. (1966). Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady, 101, 707.Google Scholar logo with link to Google Scholar
Louviere, J. J., Flynn, T. N., & Marley, A. A. J. (2015). Best-worst scaling: Theory, methods and applications. Cambridge: Cambridge University Press. Google Scholar logo with link to Google Scholar
Luce, R. D. (2003). Whatever happened to information theory in psychology?. Review of General Psychology, 7(2), 183–188. Google Scholar logo with link to Google Scholar
Luthra, S., You, H., Rueckl, J. G., & Magnuson, J. S. (2020). Friends in Low-Entropy Places: Orthographic Neighbor Effects on Visual Word Identification Differ Across Letter Positions. Cognitive Science, 44(12), e12917. Google Scholar logo with link to Google Scholar
McClelland, J. L., & Rumelhart, D. E. (1981). An interactive activation model of context effects in letter perception: I. An account of basic findings. Psychological review, 88(5), 375. Google Scholar logo with link to Google Scholar
Milin, P., Kuperman, V., Kostic, A., & Baayen, R. H. (2009). Paradigms bit by bit: An information theoretic approach to the processing of paradigmatic structure in inflection and derivation. Analogy in grammar: Form and acquisition, 3811, 214–252. Google Scholar logo with link to Google Scholar
Miller, R. R., Barnet, R. C., & Grahame, N. J. (1995). Assessment of the Rescorla-Wagner model. Psychological Bulletin, 117(3), 363. Google Scholar logo with link to Google Scholar
O’Regan, J. K., Lévy-Schoen, A., Pynte, J., & Brugaillière, B. (1984). Convenient fixation location within isolated words of different length and structure. Journal of Experimental Psychology: Human Perception and Performance, 10(3), 393.Google Scholar logo with link to Google Scholar
Peereman, R., & Content, A. (1997). Orthographic and phonological neighborhoods in naming: Not all neighbors are equally influential in orthographic space. Journal of Memory and language, 37(3), 382–410. Google Scholar logo with link to Google Scholar
Perea, M., & Rosa, E. (2000). The effects of orthographic neighborhood in reading and laboratory word identification tasks: A review. Psicológica, 21(2), 327–340.Google Scholar logo with link to Google Scholar
Piantadosi, S. T., Tily, H., & Gibson, E. (2011). Word lengths are optimized for efficient communication. Proceedings of the National Academy of Sciences, 108(9), 3526–3529. Google Scholar logo with link to Google Scholar
Pothos, E. (2010). An entropy model for artificial grammar learning. Frontiers in psychology, 11, 16. Google Scholar logo with link to Google Scholar
Ramscar, M., Dye, M., & Klein, J. (2013). Children value informativity over logic in word learning. Psychological science, 24(6), 1017–1023. Google Scholar logo with link to Google Scholar
Ramscar, M., & Port, R. F. (2016). How spoken languages work in the absence of an inventory of discrete units. Language Sciences, 531, 58–74. Google Scholar logo with link to Google Scholar
Rayner, K., & Kaiser, J. S. (1975). Reading mutilated text. Journal of Educational Psychology, 671, 301–306. Google Scholar logo with link to Google Scholar
Rayner, K., White, S. J., Johnson, R. L., & Liversedge, S. P. (2006). Raeding wrods with jubmled lettres: There is a cost. Psychological Science, 171, 192–193. Google Scholar logo with link to Google Scholar
Rescorla, R. (1988). Pavlovian conditioning: it’s not what you think it is. American Psychologist, 431, 151–160. Google Scholar logo with link to Google Scholar
Rumelhart, D. E., & McClelland, J. L. (1982). An interactive activation model of context effects in letter perception: II. The contextual enhancement effect and some tests and extensions of the model. Psychological review, 89(1), 60. Google Scholar logo with link to Google Scholar
Shannon, C. E. (1948). A note on the concept of entropy. Bell System Technical Journal, 271, 379–423. Google Scholar logo with link to Google Scholar
Sears, C. R., Hino, Y., & Lupker, S. J. (1995). Neighborhood size and neighborhood frequency effects in word recognition. Journal of Experimental Psychology: Human Perception and Performance, 21(4), 876.Google Scholar logo with link to Google Scholar
Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 271, 379–423 & 623–656. Google Scholar logo with link to Google Scholar
(1956). The bandwagon. IRE Transactions on Information Theory, 2(1), 3. Google Scholar logo with link to Google Scholar
Shaoul, C. & Westbury, C. (2006). USENET Orthographic Frequencies for 111,627 English Words. (2005–2006) Edmonton, AB: University of Alberta (downloaded from [URL])
Siakaluk, P. D., Sears, C. R., & Lupker, S. J. (2002). Orthographic neighborhood effects in lexical decision: The effects of nonword orthographic neighborhood size. Journal of Experimental Psychology: Human Perception and Performance, 28(3), 661.Google Scholar logo with link to Google Scholar
Sutton, R., & Barto, A. (1998). Reinforcement learning. Cambridge, MA: MIT Press.Google Scholar logo with link to Google Scholar
Takahashi, T. (2013). A psychophysical theory of Shannon entropy. Neuroendocrinology Letters, 34(7), 615–617.Google Scholar logo with link to Google Scholar
Treiman, R. (1985). Onsets and rimes as units of spoken syllables: Evidence from children. Journal of experimental child psychology, 39(1), 161–181. Google Scholar logo with link to Google Scholar
(1986). The division between onsets and rimes in English syllables. Journal of Memory and Language, 25(4), 476–491. Google Scholar logo with link to Google Scholar
Treiman, R., Fowler, C. A., Gross, J., Berch, D., & Weatherston, S. (1995). Syllable structure or word structure? Evidence for onset and rime units with disyllabic and trisyllabic stimuli. Journal of Memory and Language, 34(1), 132–155. Google Scholar logo with link to Google Scholar
Westbury, C. F., & Hollis, G. (2007). Putting Humpty together again: Synthetic approaches to nonlinear variable effects underlying lexical access. In G. Jarema & G. Libben (Eds.), The Mental Lexicon: Core perspectives (pp. 7–30). Bingley: Emerald. Google Scholar logo with link to Google Scholar
Westbury, C., Shaoul, C., Moroschan, G., & Ramscar, M. (2016). Telling the world’s least funny jokes: On the quantification of humor as entropy. Journal of Memory and Language, 861, 141–156. Google Scholar logo with link to Google Scholar
Westbury, C., Yang, M., and Anderson, K. (2024). The Principal Components of Meaning, Revisited. [Accepted for publication in Psychonomic Bulletin & Review]Google Scholar logo with link to Google Scholar
Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B), 73(1), 3–36. Google Scholar logo with link to Google Scholar
Yarkoni, T., Balota, D., & Yap, M. (2008). Moving beyond Coltheart’s N: A new measure of orthographic similarity. Psychonomic Bulletin & Review, 15(5), 971–979. Google Scholar logo with link to Google Scholar
Zhang, J. W., & Wang, Q. H. (2010). The orthographic neighborhood effect in word recognition. Advances in Psychological Science, 18(06), 892–899.Google Scholar logo with link to Google Scholar
Ziegler, J. C., & Perry, C. (1998). No more problems in Coltheart’s neighborhood: Resolving neighborhood conflicts in the lexical decision task. Cognition, 68(2), B53–B62. Google Scholar logo with link to Google Scholar
Zipf, G. K. (1936). The Psychobiology of Language. Houghton-Mifflin: New York, NY.Google Scholar logo with link to Google Scholar
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